Questions tagged [sensitivity-analysis]

Auxiliary methods intended to check if the outcome of an analysis strongly depends on the model assumptions, preprocessing steps, presence of outliers, etc.

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Test if difference between two groups across 4 categories [duplicate]

I want to make a sensitivity analysis to test whether there is a statistically significant difference between the observations included for the study (n = 100,000) and the excluded observations (n = ...
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Interpretation of sensitivity analysis provided by tgp package

I have a question about the interpretation of the sensitivity analysis I obtained by R's tgp package (https://cran.r-project.org/web/packages/tgp/vignettes/tgp2.pdf) as can be seen below. The relation ...
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How to deal with missing value in dependent variable of prediction model?

I am trying to build a prediction model from longitudinal study after intervention. So after intervention, we followup patients 1,3, and 6 months later to see if they are cured or not. So dependent ...
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How to conduct sensitivity analyses for cluster / sample in three-level meta analyses in metafor?

Problem am wanting to do a leave1out sensitivity analysis of an rma.mv (three-level) meta-analysis, where "1" is a cluster / sample (rather than a single effect size). I understand that some ...
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Two-sample Kolmogorov Smirnov test for global sensitivity analysis - How to treat discrete distributions?

Dear Cross Validated community, We are working on a uncertainty & sensitivity analysis using a mathematical optimization model. More specifically, we have a set of uncertain parameters, which ...
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Sensitivity analysis of a fuzzy cognitive model

I have a fuzzy cognitive model of inter-organizational collaboration that is represented by a 27x27 matrix. I want to analyze the effects of individual variables. I've read I can do this best with a ...
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Comparing Sensitivities of Microscopic and Macroscopic Models (Travel Demand)

A simulation output for each microscopic travel demand model and two further macroscopic MDCEV models - transformed versions of the microscopic model - are given for a base scenario. Moreover, three ...
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What sensitivity analysis technique is suitable for nominal parameters?

I'm currently developing an agent-based model and at the stage of verification and validation. I'm new to sensitivity analysis and was wondering if you know of any sensitivity analysis techniques to ...
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Is Sensitivity Analysis for Making Causal Inferences Only for Backdoor Adjustment?

I am wondering if sensitivity analysis for causal inference is only applicable when doing backdoor adjustment/selecting on observables. Conventionally, sensitivity analysis evaluates the threat of an ...
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Distribution of Sobol's Indices

Some background: Given a linear regression model (or any other GLM), we all know how to test the null hypothesis $\hat\beta_i=0$. The lm function in ...
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How to convert Cohen's f to Eta-sqaure for mixed ANOA?

I did a sensitivity analysis with G*power to calculate the Cohen's f for a 2x2 Mixed ANOVA. I'd like to convert the Cohen's f from the result to eta-square. I' ve found formula for conversion that ...
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Bounds of Shapley values for variable importance

Imagine you have either a very good predictive model $f$ for a response $y$ or two highly predictive models $f_1$ and $f_2$. Is it possible to bound the "true" Shapley values of $y$ in ...
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Interpretation unmeasured continuous confounder impact in logistic regression with tipr

I am using the tipr library in R to evaluate how “big” should be the effect of an unmeasured continuous confounder U on the outcome of a logistic regression model to nullify the effect found in an ...
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looking for reference on sensitivity analysis of continuous outcome in causal inference

$Q:$ I knew there are methods for binary outcome variable sensitivity analysis in causal inference. However, I have not found methods for continuous outcome variable sensitivity analysis(SUTVA or ...
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Generalizability of Sensitivity Analysis Results

I have executed an analysis using the novel approach developed by Imai et al. (2021) that allows for the execution of matching/weighting techniques when using panel data. I was planning on running ...
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Sensitivity analysis based on a dataset with dependent variables

I am part of a company that produces data from satellite images and machine learning/deep learning. We produce different data and sometimes the results of one step will be used as input for the next ...
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Updated: How to do a sensitivity analysis for an ordered logistic regression in R?

How would you do an robustness or sensitivity analysis for an ordered logistic regression? Can it be done by replacing the control variables with others that are similar in the model? For instance ...
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How do I change the model specification and conduct sensitivity analysis in mice's imputation procedure for categorical variable?

I am interested in conducting sensitivity analysis of multiple imputation by mice on a binary categorical variable. mice used logistic regression to conduct imputation. How do I modify the parameter ...
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Sensitivity Analysis for Measurement Error

I have a binary outcome variable and two predictors, B1 and B2. B1 is a much stronger predictor than B2 (for simplicity, assume an OLS model but this is also true when using a logit or probit). One ...
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How can I learn to refutation methods in DoWhy Library?

The refutation methods in the dowhy causal inference library are useful. https://www.pywhy.org/dowhy/v0.8/dowhy.causal_refuters.html I'd like to learn a theory that explains these methods well, but I'...
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Does Rubin-Rosenbaum sensitivity analysis include selection bias as special case?

According to pg 2 of the pdf http://www-stat.wharton.upenn.edu/~rosenbap/BehStatSen.pdf, "however, this alternative method takes account of sampling variability and is applicable to any kind of ...
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What is the difference between the sensitivity analysis and feature importance

Pardon my question, but google does not find any resource on this topic. My question is related to more if they are the same field, can they be used interchangeably or not. As I understand in the ...
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sensitivity analysis for a glm

I want to know how to make a sensitivity analysis for a glm. I checked mulsisensi package and seems to wok with other type of models. Actually I would like something like the lek method but seems to ...
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How do I complete a sensitivity analysis to simulate censored data?

I am currently trying to analyze the duration of the egg stage of two species of insect (factor 1; 2 levels - HA and AP) at several different temperatures (factor 2: 5 levels - 20, 23, 26, 29, 32) ...
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Morris screening: how to compare quantities of interest?

To find out which parameters are most influential in my simulation model, I'm using Morris screening. To summarise the output of my model (time sequence) I'm using four different scalar quantities of ...
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Sensitivity Analysis: Conceptual differences between "competing risks" in RCT and immortal time bias in longitudinal retrospective observation data

I'm reading a review-methodology article on Sensitivity Analysis in Randomized Controlled Trials (RCT): https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-13-92 Section (page 7 of ...
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Sensitivity to settings vs statistical sensitivity

I am confused between two different properties of a model. Both are called sensitivity. First, sensitivity is another word for true positive rate. Second, the output of a model changes in response to ...
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Log of a log-transformed variable

I have been suggested to use the log of a log-transformed independent variable (i.e., log(log healthcare expenditure)). I am not sure how would this make sense. Is this a standard practice (in the ...
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Does it make sense to do a prior sensitivity analysis if using flat priors (Mplus default)?

If it does make sense to do a sensitivity analysis how should one determine which priors to use? If flat "non-informative" priors are chosen to begin with because of a lack of information ...
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R2 limitation for linearity and monotonic?

I'm studying sensitivity analysis. I know that to use PCC and SRC, linearity and/or monotonic must be assumed. So I'm trying to calculate R² for this, and my question for this is: Can I use GLM to ...
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A few queries regarding Meta-analysis and its subparts using R

As part of a meta-analysis of a disease in R, I received some feedback on the paper of which it is a part by external consultants. I cannot contact them again, and am confused about the advice they ...
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Understanding "In Bayesian inference, the difference between data and a parameter is that one is observed (data) and one isn't (parameter)" [duplicate]

In his statistical rethinking course, Richard Mclreath states "In Bayesian inference, the difference between data and a parameter is that one is observed (data) and one isn't (parameter)" I ...
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Inferring effect & effect modification from simulation data

I have a "black box" system (computer simulation), which takes inputs: $x_1 \in [0,1]$, $x_2 \in [0,1]$, and $N_i$ others $\vec\theta = \{\theta_1, \dots, \theta_{N_i}\}$, and produces an ...
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When do we do sensitivity analysis in biostat and how do we do it?

I have two questions below. I have seen people doing sensitivity analysis in observational study papers for the model to check sensitivity to assumptions in bayesian context for selection of priors. I ...
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Can a global sensitivity analysis be performed on Bayesian inference?

My question is, is it possible to perform a Global Sensitivity Analysis on a Bayesian inference model (not just on the prior, the entire model)? A bit of context: I am fairly new to Bayesian ...
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How to conduct sensitivity analysis on IPSW for survival data?

I am working on survival data, comparing two groups of patients (with or without treatment). These data have some selection bias, thus, I have chosen to weight my sample by Inverse Propensity Score ...
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Is there a method for Global Sensitivity Analysis that does not require special sampling methods?

I want to perform a global sensitivity analysis using randomly sampled data that already exists (or can be generated with only N randomized model runs). The impetus for this is to be able to use the ...
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Interpretation of the E-value for non-epidemiologists

A recent method for sensitivity analysis is the E-value (VanderWeele and Ping, 2017). Yet, I'm still struggling with the interpretation of such a value. Coming from outside of epidemiology, where risk ...
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Trade-off between omitting variables or dropping observations in multivariate logistic regression

Say you are selecting $n$ observations from a complex survey of $N$ individuals to create an analytical sample of relevant observations; and that you intend to fit a binomial multivariate logistic ...
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What-If Scenario Regression Modelling

I'm pondering a scenario involving some insurance data but this could be relevant in many fields. The idea is that I have a total count of some event. Let's imagine this count is the # of attorney ...
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what is the sensitivity of the neural network using standardized input

Suppose I trained a neural network with standardisation of the data following (X-EX)/std(X). The input is x(t) and output is y(t). How can I calculate the sensitivity of this trained network (...
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Can I perform sensitivity analysis, if I don't know expected prediction results?

Can I perform sensitivity analysis, if I don't know expected prediction results? I.e. I have a model with input parameters and weights. But I don't know when a prediction should be true and when false....
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How do you deal with A/B testing for small samples?

I am performing A/B testing (basically hypothesis testing) with relatively small samples, so the results are largely inconclusive. I am aware of techniques like CUPED (for decreasing the sample ...
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Difference-in-Difference regression model for sensitivity analysis

I have 5-year sales information from a grocery store in Canada. I want to check whether an event that happened in 2017, affected the effect of the price of a product on its sales. For example, imagine ...
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Estimating forecasting error in multi-step process

Suppose a process which looks something like this time series 1 --> model 1 --> time series 2 --> model 2 --> time series 3 An initial time series, which is a forecast, is used as input ...
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How many percentage to randomize and how many iterations in a "what-if analysis"?

I've got complete separated data as such: ...
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Sensitivity analysis after multiple imputation

After reviewing the literature there seems to be little consensus regarding the best way of performing sensitivity analysis following multiple imputation for missing values. However, the growing ...
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Using importance sampling for prior sensitivity analysis in Bayesian modeling

I read a section on Bayesian sensitivity analysis in the following book by Carlin and Louis (2009), 'Bayesian Methods for Data Analysis' (3rd ed.), CRC Press. The context is a sensitivity analysis of ...
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Sensitivity analysis using R's mice package with multiple missing variables

I am using mice to multiply impute data on a dataset with many variables with missing values. I followed this vignette to do a sensitivity analysis to understand how the imputations are influenced by ...
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Interpretation of regression coefficients after power transform (possibily with polynomial transform and PCA) [duplicate]

In general I standardize my features before regression by subtracting the mean and dividing by unit variance: $$ \hat{X} = \frac{X - \bar{X}}{Var(X)}$$ With this basic standardization, interpreting ...
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